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Attenuation theory, also known as Treisman's attenuation model, is a model of selective attention proposed by Anne Treisman, and can be seen as a revision of Donald Broadbent's filter model. Treisman proposed attenuation theory as a means to explain how unattended stimuli sometimes came to be processed in a more rigorous manner than what ...
This fueled the development of the memory selection model, which shares the same basic principle of early selection models that stimulus features are selected via their physical properties. [3] However, attended and unattended information pass through the filter, to a second stage of selection on the basis of semantic characteristics or message ...
Feature integration theory is a theory of attention developed in 1980 by Anne Treisman and Garry Gelade that suggests that when perceiving a stimulus, features are "registered early, automatically, and in parallel, while objects are identified separately" and at a later stage in processing.
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.
Illustration of regression dilution (or attenuation bias) by a range of regression estimates in errors-in-variables models. Two regression lines (red) bound the range of linear regression possibilities. The shallow slope is obtained when the independent variable (or predictor) is on the abscissa (x-axis).
As hand-crafting weights defeats the purpose of machine learning, the model must compute the attention weights on its own. Taking analogy from the language of database queries, we make the model construct a triple of vectors: key, query, and value. The rough idea is that we have a "database" in the form of a list of key-value pairs.
Bayesian model reduction was subsequently generalised and applied to other forms of Bayesian models, for example parametric empirical Bayes (PEB) models of group effects. [2] Here, it is used to compute the evidence and parameters for any given level of a hierarchical model under constraints (empirical priors) imposed by the level above.
Many theoretical studies ask how the nervous system could implement Bayesian algorithms. Examples are the work of Pouget, Zemel, Deneve, Latham, Hinton and Dayan. George and Hawkins published a paper that establishes a model of cortical information processing called hierarchical temporal memory that is based on Bayesian network of Markov chains ...